The present invention relates to detection of image features, and more particularly to feature detection in ultrasound images.
Ultrasound is a commonly used medical imaging modality. Compared to other medical imaging modalities, such as X-ray, magnetic resonance (MR), and positron emission tomography (PET), ultrasound has many advantages, as ultrasound is fast, portable, relatively low cost, and presents little risk to patients.
One limitation of ultrasound is image quality. Ultrasound images are often corrupted by speckle resulting from the coherent accumulation of random scattering in a resolution cell of the ultrasound beam. While the texture of the speckle does not correspond to any underlying structure, the local brightness of the speckle pattern is related to the local echogenicity of underlying scatterers. The speckle can have a detrimental effect on image quality and interpretability, and can cause inaccurate feature detection in ultrasound images.
Conventional approaches, such as the Canny edge detector, commonly detect features in images based on gradient operators. Often this is achieved by convolution of the image with a bandpass kernel K, which can be modeled as the derivative of a Gaussian function,
                    K        =                              ∂                          ∂              x                                ⁢                      (                                          1                                                                            2                      ⁢                      π                                                        ⁢                  σ                                            ⁢                              ⅇ                                                      -                                          x                      2                                                        /                                      (                                          2                      ⁢                                              σ                        2                                                              )                                                                        )                                                  =                                            -              x                                                                        2                  ⁢                  π                                            ⁢                              σ                3                                              ⁢                      ⅇ                                          -                                  x                  2                                            /                              (                                  2                  ⁢                                      σ                    2                                                  )                                                        where σ2 is the variance. The gradient can then be defined as Gx=K*I, Gy=KT*I, where I is the image and K is a 1D horizontal kernel. A feature map for identifying features in the image has a value equal to the gradient magnitude, F=√{square root over (Gx2+Gy2)} for each pixel in the image However, since the gradient of an image is sensitive to the speckle, the speckle can adversely affect the feature map, leading to inaccurate feature detection. While increasing the variance may help to blur over the speckle, the effect of the speckle is often still apparent in feature maps generated using conventional methods. Furthermore, larger variances also blur edges in images, making actual image features of images more difficult to detect.